LID SPN TRAP

1422697_s_at

Jarid2 jumonji, AT rich interactive domain 2

AIM Score vs. Gene Expression
Full X range:
Auto X range:
Group Comparisons: Boxplots

CP73

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
0.017 0.896 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.689
Model: OLS Adj. R-squared: 0.640
Method: Least Squares F-statistic: 14.05
Date: Thu, 21 Nov 2024 Prob (F-statistic): 4.61e-05
Time: 04:50:42 Log-Likelihood: -99.662
No. Observations: 23 AIC: 207.3
Df Residuals: 19 BIC: 211.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -155.7800 213.808 -0.729 0.475 -603.285 291.725
C(dose)[T.1] 675.3780 399.074 1.692 0.107 -159.893 1510.649
expression 21.6741 22.060 0.983 0.338 -24.498 67.846
expression:C(dose)[T.1] -62.0066 39.676 -1.563 0.135 -145.049 21.035
Omnibus: 0.004 Durbin-Watson: 1.850
Prob(Omnibus): 0.998 Jarque-Bera (JB): 0.174
Skew: -0.020 Prob(JB): 0.917
Kurtosis: 2.575 Cond. No. 1.14e+03

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.614
Method: Least Squares F-statistic: 18.52
Date: Thu, 21 Nov 2024 Prob (F-statistic): 2.81e-05
Time: 04:50:42 Log-Likelihood: -101.05
No. Observations: 23 AIC: 208.1
Df Residuals: 20 BIC: 211.5
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 29.9391 184.040 0.163 0.872 -353.961 413.840
C(dose)[T.1] 52.0147 13.315 3.906 0.001 24.239 79.790
expression 2.5050 18.985 0.132 0.896 -37.098 42.108
Omnibus: 0.295 Durbin-Watson: 1.905
Prob(Omnibus): 0.863 Jarque-Bera (JB): 0.469
Skew: 0.062 Prob(JB): 0.791
Kurtosis: 2.311 Cond. No. 424.

Model:
AIM ~ C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.649
Model: OLS Adj. R-squared: 0.632
Method: Least Squares F-statistic: 38.84
Date: Thu, 21 Nov 2024 Prob (F-statistic): 3.51e-06
Time: 04:50:42 Log-Likelihood: -101.06
No. Observations: 23 AIC: 206.1
Df Residuals: 21 BIC: 208.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 54.2083 5.919 9.159 0.000 41.900 66.517
C(dose)[T.1] 53.3371 8.558 6.232 0.000 35.539 71.135
Omnibus: 0.322 Durbin-Watson: 1.888
Prob(Omnibus): 0.851 Jarque-Bera (JB): 0.485
Skew: 0.060 Prob(JB): 0.785
Kurtosis: 2.299 Cond. No. 2.57

Model:
AIM ~ expression

OLS Regression Results
Dep. Variable: AIM R-squared: 0.382
Model: OLS Adj. R-squared: 0.352
Method: Least Squares F-statistic: 12.97
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00168
Time: 04:50:42 Log-Likelihood: -107.57
No. Observations: 23 AIC: 219.1
Df Residuals: 21 BIC: 221.4
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept -500.1370 161.101 -3.105 0.005 -835.164 -165.110
expression 58.3299 16.196 3.602 0.002 24.649 92.011
Omnibus: 1.873 Durbin-Watson: 2.292
Prob(Omnibus): 0.392 Jarque-Bera (JB): 1.482
Skew: 0.454 Prob(JB): 0.477
Kurtosis: 2.151 Cond. No. 285.

CP101

Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)

F-statistic p-value df difference
0.014 0.908 1.0

Model:
AIM ~ expression + C(dose) + expression:C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.506
Model: OLS Adj. R-squared: 0.371
Method: Least Squares F-statistic: 3.750
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0446
Time: 04:50:42 Log-Likelihood: -70.017
No. Observations: 15 AIC: 148.0
Df Residuals: 11 BIC: 150.9
Df Model: 3
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 334.7633 286.169 1.170 0.267 -295.090 964.616
C(dose)[T.1] -362.7118 368.085 -0.985 0.346 -1172.862 447.438
expression -26.4492 28.290 -0.935 0.370 -88.716 35.817
expression:C(dose)[T.1] 41.2196 36.859 1.118 0.287 -39.906 122.345
Omnibus: 1.795 Durbin-Watson: 0.968
Prob(Omnibus): 0.408 Jarque-Bera (JB): 1.143
Skew: -0.659 Prob(JB): 0.565
Kurtosis: 2.700 Cond. No. 658.

Model:
AIM ~ expression + C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.358
Method: Least Squares F-statistic: 4.897
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.0279
Time: 04:50:42 Log-Likelihood: -70.824
No. Observations: 15 AIC: 147.6
Df Residuals: 12 BIC: 149.8
Df Model: 2
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 89.3260 185.552 0.481 0.639 -314.957 493.609
C(dose)[T.1] 48.5044 16.784 2.890 0.014 11.935 85.074
expression -2.1665 18.323 -0.118 0.908 -42.088 37.755
Omnibus: 2.875 Durbin-Watson: 0.787
Prob(Omnibus): 0.238 Jarque-Bera (JB): 1.977
Skew: -0.870 Prob(JB): 0.372
Kurtosis: 2.630 Cond. No. 238.

Model:
AIM ~ C(dose)

OLS Regression Results
Dep. Variable: AIM R-squared: 0.449
Model: OLS Adj. R-squared: 0.406
Method: Least Squares F-statistic: 10.58
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.00629
Time: 04:50:42 Log-Likelihood: -70.833
No. Observations: 15 AIC: 145.7
Df Residuals: 13 BIC: 147.1
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 67.4286 11.044 6.106 0.000 43.570 91.287
C(dose)[T.1] 49.1964 15.122 3.253 0.006 16.527 81.866
Omnibus: 2.713 Durbin-Watson: 0.810
Prob(Omnibus): 0.258 Jarque-Bera (JB): 1.868
Skew: -0.843 Prob(JB): 0.393
Kurtosis: 2.619 Cond. No. 2.70

Model:
AIM ~ expression

OLS Regression Results
Dep. Variable: AIM R-squared: 0.066
Model: OLS Adj. R-squared: -0.006
Method: Least Squares F-statistic: 0.9219
Date: Thu, 21 Nov 2024 Prob (F-statistic): 0.354
Time: 04:50:42 Log-Likelihood: -74.786
No. Observations: 15 AIC: 153.6
Df Residuals: 13 BIC: 155.0
Df Model: 1
coef std err t P>|t| [95.0% Conf. Int.]
Intercept 298.6765 213.739 1.397 0.186 -163.080 760.433
expression -20.6307 21.487 -0.960 0.354 -67.050 25.788
Omnibus: 1.955 Durbin-Watson: 1.566
Prob(Omnibus): 0.376 Jarque-Bera (JB): 0.962
Skew: 0.128 Prob(JB): 0.618
Kurtosis: 1.786 Cond. No. 219.